Magnetic Susceptibility of Soils from Eastern Botswana: A Reconnaissance Survey and Potential Applications
Bibliographic record
Abstract
<p class="zhengwen">Soil magnetic properties measurements are relatively fast and inexpensive but have been proved to be sufficient for preliminary investigations in diverse socio-developmental issues. This paper presents results of a reconnaissance study of soil colour and magnetic susceptibility (c) in eastern Botswana, where ~80% of the population resides. The work is a first step to creating a database of rock and soil magnetic properties and to document spatial variations in magnetic properties in the country. These measurements are important as constraints for interpretation of available aeromagnetic data and can also be exploited for environmental soil research (pollution) and land-use planning (agriculture). The soils sampled include derivatives of varying types and provenance such as Archean gneissic granitoids, metamorphosed rocks (granulites), volcano-sedimentary assemblages, Karoo basalts, and alluvial sediments. A soil colour chart was used since soil colours and magnetic properties are diagnostic of its parent rock sources and weathering profiles. Soil magnetic susceptibilities were measured at both low frequency (0.46 MHz, clf) and high frequency (4.6 MHz, chf), thus allowing calculation of frequency-dependent susceptibility (cfd, cfd%) for detecting ultra-fine ferromagnetic minerals.</p>It was found that soils with Hues ranging from 7.5YR to 10YR have appreciable amount of magnetic materials and soils with Hues of 2.5YR are generally nonmagnetic. The results of soil magnetic susceptibility profiles show spatial variation closely related to the variation in basement rocks, which provides excellent evidence that the magnetic susceptibility variation reflects basement rocks or bedrock composition (soil parent material). In relation to the Botswana physiographic units, soils from the hardveld (Precambrian) show the highest susceptibilities, followed by those from the sandveld, with the lowest values being from the alluvial. The frequency dependent magnetic susceptibilities indicate the presence of ultra-fine super-paramagnetic minerals such as magnetite/maghemite. It is suggested that a systematic and continuous programme of rock and soil magnetic measurements would benefit various socio-economic and development priority sectors of Botswana. This also applies to many developing countries in Africa where soil physics and measurement of soil susceptibility in particular, is generally still at an embryonic stage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".